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dc.contributor.authorMöllenhoff, Kathrin-
dc.contributor.authorLoingeville, Florence-
dc.contributor.authorBertrand, Julie-
dc.contributor.authorNguyen, Thu Thuy-
dc.contributor.authorSharan, Satish-
dc.contributor.authorSun, Guoying-
dc.contributor.authorGrosser, Stella-
dc.contributor.authorZhao, Liang-
dc.contributor.authorFang, Lanyan-
dc.contributor.authorMentré, France-
dc.contributor.authorDette, Holger-
dc.date.accessioned2019-09-10T09:07:58Z-
dc.date.available2019-09-10T09:07:58Z-
dc.date.issued2019-
dc.identifier.urihttp://hdl.handle.net/2003/38213-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-20192-
dc.description.abstractThe classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard the PK parameters AUC and Cmax are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency the formulations are deemed to be similar if the 90%- confidence interval for these ratios falls between 0:8 and 1:25. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of AUC and Cmax using non-linear mixed effects models. Here we propose another test than the TOST, called BOT, and evaluate it through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of 0:05, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;21/2019-
dc.subjectbioequivalenceen
dc.subjecttwo one-sided testsen
dc.subjectnoncompartmental bioequivalence analysisen
dc.subjectpharmacokineticsen
dc.subjectnonlinear mixed effects modelen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleEfficient model-based bioequivalence testingen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
eldorado.secondarypublicationfalsede
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